code: def test_sst_dst_to_dense(
      unused1, sst, dst, expd_rt, dim, unused2, unused3
  ):
      """Tests the correct expected reconstruction from frozen sst and dst tensors."""
      sparser = SignalSparsity(
          sst_top_k_element=1,
          sst_top_k_dim=dim,
          dst_top_k_element=1,
          dst_top_k_dim=dim,
      )
      dense_recons = sparser.sst_dst_to_dense(sst, dst)
      objects_are_equal(
          dense_recons,
          expd_rt,
          raise_exception=True,
          rtol=RTOL,
          atol=ATOL,
      )
  
file_path: fairscale/tests/experimental/wgit/test_signal_sparsity.py
imports: import pytest
  import torch
  from fairscale.experimental.wgit.signal_sparsity import SignalSparsity, random_sparse_mask
  from fairscale.fair_dev.testing.testing import objects_are_equal
label_desc: Use image node_id 9 for calling a global function with example usage: test_sst_dst_to_dense(unused1, sst, dst, expd_rt, dim, unused2, unused3) without return types